Mathematical Statistics with Resampling and R

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John Wiley & Sons, Sep 17, 2018 - Mathematics - 560 pages
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This thoroughly updated second edition combines the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The second edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book has been classroom-tested to ensure an accessible presentation, uses the powerful and flexible computer language R for data analysis and explores the benefits of modern resampling techniques.

This book offers an introduction to permutation tests and bootstrap methods that can serve to motivate classical inference methods. The book strikes a balance between theory, computing, and applications, and the new edition explores additional topics including consulting, paired t test, ANOVA and Google Interview Questions. Throughout the book, new and updated case studies are included representing a diverse range of subjects such as flight delays, birth weights of babies, and telephone company repair times. These illustrate the relevance of the real-world applications of the material. This new edition:

• Puts the focus on statistical consulting that emphasizes giving a client an understanding of data and goes beyond typical expectations

• Presents new material on topics such as the paired t test, Fisher's Exact Test and the EM algorithm

• Offers a new section on "Google Interview Questions" that illustrates statistical thinking

• Provides a new chapter on ANOVA

• Contains more exercises and updated case studies, data sets, and R code

Written for undergraduate students in a mathematical statistics course as well as practitioners and researchers, the second edition of Mathematical Statistics with Resampling and R presents a revised and updated guide for applying the most current resampling techniques to mathematical statistics.

 

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Contents

Chapter 1 Data and Case Studies
1
Chapter 2 Exploratory Data Analysis
21
Permutation Tests
47
Chapter 4 Sampling Distributions
75
The Bootstrap
103
Chapter 6 Estimation
149
Chapter 7 More Confidence Intervals
187
Chapter 8 More Hypothesis Testing
241
Chapter 12 Oneway ANOVA
419
Chapter 13 Additional Topics
433
Appendix A Review of Probability
477
Appendix B Probability Distributions
487
Appendix C Distributions Quick Reference
509
Solutions to Selected Exercises
513
References
525
Index
531

Chapter 9 Regression
297
Chapter 10 Categorical Data
359
Chapter 11 Bayesian Methods
391
EULA
538
Copyright

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About the author (2018)

LAURA M. CHIHARA, PHD, is Professor of Mathematics and Statistics at Carleton College. She has extensive experience teaching mathematics and statistics and has worked as Educational Services Supervisor at Insightful Corporation.

TIM C. HESTERBERG, PHD, is Senior Data Scientist at Google. He was a senior research scientist for Insightful Corporation and led the development of S+Resample and other S+ and R software.

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